CN110139070B - Intelligent environment monitoring method, system and equipment based on deep learning - Google Patents
Intelligent environment monitoring method, system and equipment based on deep learning Download PDFInfo
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Abstract
The invention discloses an intelligent environment monitoring method, an intelligent environment monitoring system and intelligent environment monitoring equipment based on deep learning. Wherein the method comprises the following steps: the network camera can shoot images of an environment space, the monitoring center can collect and record images shot by the network camera where the far-end environment space is located in real time through the internet of things, the image recognition module recognizes whether the current environment of the environment space is a scene where the use of the networked communication equipment is forbidden according to the images shot by the network camera where the far-end environment space is located in real time through the internet of things and obtains an environment scene recognition result, wherein the scene where the use of the networked communication equipment is forbidden comprises scenes such as class or meeting. Through the mode, the environment can be effectively monitored.
Description
Technical Field
The invention relates to the technical field of environment and communication, in particular to an intelligent environment monitoring method, system and device based on deep learning.
Background
In 2018, the world health organization formally lists the 'network game addiction disease' in the range of mental diseases, the relevant regulations take effect from 19 days 6 months, the mental disease classification listed in the eleventh edition of statistical classification of international diseases and related health problems, and the mental diseases which are accompanied by abuse of alcohol, tobacco, caffeine, drugs and drugs are addictive mental diseases, which is equal to the recognition that addiction to playing games is a major public health issue. Although internet addiction is not classified as a disease by authorities, many have suffered from it and courses and agencies for treating internet addiction have begun to emerge in the united states. A cross-country survey of OECD (Organization for Economic Co-operation and Development) showed that the 5 most serious areas of online game addiction were in asia, hong kong, korea, taiwan, singapore and mainland china. The popularization rate of the network and the intelligent mobile phone is high, and the cultural background of Asian region which takes the advancement as the guide makes the young students easily release the pressure in the network world. In france, 9 months 2018, the use of cell phones in schools is mandatory by the means of administrative legislation for primary and secondary school students, and in addition to cell phones, the new act also stipulates the prohibition of the use of all networkable communication devices including tablet computers and watches.
In the prior art, various environments at all levels generally adopt a camera to record video and sound, a remote monitoring center appropriately monitors and controls the environment, and people carry various wireless communication equipment which can be networked to enter the environment and use the environment, so that the learning or working efficiency is poor, and effective assistance cannot be provided. Along with the spread of addiction to online games, various wireless communication devices capable of being networked are randomly used in various environments, so that negative learning or working effects of personnel are seriously caused, and the problem of headache which is impossible to prevent for education or management personnel is caused.
The existing mobile phone signal and wireless network signal shielding technology aims at the continuous development of communication systems, and can be used for isolating mobile phone signals and wireless signals of various communication types within the shielding range according to the actual situation of mobile communication inside and outside a country, namely about 10-250 square meters, so that a mobile phone can not be used for calling and answering, can not send or receive short messages, can not be networked, and can not interfere the work of other electronic equipment, thereby ensuring various characteristics required by the place. On the other hand, the mobile phone can be recovered to be normally used after leaving the partition range, and the mobile phone has no damage to the human body.
The chinese patent of "CN 107734301A a security monitoring system for classroom" and the chinese patent of "CN 108594740A a media classroom integrated monitoring system" both adopt a common image acquisition function in the environment and each corresponding additional security or monitoring function, and carry various kinds of wireless communication devices that can be networked to people to enter the environment and use, resulting in poor learning or working efficiency and failing to provide an effective solution or improvement scheme.
The chinese patent invention "CN 107359957A mobile phone signal shielding device" only provides shielding measures for some mobile phone signals, which can achieve the characteristics of environmental silence and safety, but does not discuss and shield all the mobile phone signals and wireless networking signals.
However, the inventors found that at least the following problems exist in the prior art:
the other scheme is to provide shielding measures only for some mobile phone signals, which can achieve the characteristics of environmental silence, safety and the like, but does not comprehensively discuss the shielding possibility for the comprehensive mobile phone signals and wireless networking signals, so that the existing intelligent environment monitoring scheme can not realize effective environment monitoring.
Disclosure of Invention
In view of this, the present invention provides an intelligent environment monitoring method, system and device based on deep learning, which can realize effective environment monitoring.
According to one aspect of the invention, an intelligent environment monitoring method based on deep learning is provided, which comprises the following steps:
the network camera shoots an image of an environment space;
the monitoring center collects and records images shot by the network camera in real time, wherein the network camera is located in a far-end environment space;
the image recognition module is used for recognizing whether the current environment of the environment space is a scene in which the use of the networked communication equipment is forbidden or not according to images shot by a network camera in which the remote environment space is located and collected and recorded by the Internet of things in real time by the monitoring center to obtain an environment scene recognition result; wherein the scenario in which use of the networked communication device is prohibited includes a class or meeting scenario.
The image recognition module is used for recognizing whether the current environment of the environment space is a scene for prohibiting using the networking communication equipment to obtain an environment scene recognition result according to images shot by a network camera where the remote environment space is located, which are collected and recorded by the internet of things in real time by the monitoring center, and comprises the following steps:
the image recognition module is used for recognizing whether the current environment of the environment space is a scene in which the use of the networking communication equipment is forbidden or not by adopting an algorithm analysis mode based on deep learning according to images shot by a network camera in which the remote environment space is located and collected and recorded by the Internet of things in real time by the monitoring center to obtain an environment scene recognition result.
Wherein, after the image recognition module recognizes whether the current environment of the environment space is a scene in which the use of the networked communication device is forbidden to obtain the environment scene recognition result according to the images shot by the network camera in which the remote environment space is located and collected and recorded by the internet of things in real time by the monitoring center, the method further comprises the following steps:
and the signal shielding module shields the mobile phone signals and the wireless network signals of the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is the environment scene forbidden to use the networking communication equipment.
Wherein, the signal shielding module shields the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is the environment scene forbidden to use the networking communication device, and the method further comprises the following steps:
and the signal shielding module closes and shields the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is a non-forbidden environment scene, namely the environment scene allowing the networking communication equipment to be used.
According to another aspect of the present invention, there is provided an intelligent environment monitoring system based on deep learning, including:
the system comprises a network camera, a monitoring center and an image identification module;
the network camera is used for shooting images of the environment space;
the monitoring center is used for acquiring and recording images shot by the network camera in which the remote environment space is located in real time through the Internet of things;
the image identification module is used for identifying whether the current environment of the environment space is a scene in which the use of the networked communication equipment is forbidden or not according to images shot by a network camera in which the remote environment space is located, which are collected and recorded by the Internet of things in real time by the monitoring center, so as to obtain an environment scene identification result; wherein the scenario in which use of the networked communication device is prohibited includes a class or meeting scenario.
The image recognition module is specifically configured to:
and identifying whether the current environment of the environment space is a scene in which the use of the networked communication equipment is forbidden or not by adopting an algorithm analysis mode based on deep learning according to the image shot by the network camera in which the remote environment space is located, which is collected and recorded by the Internet of things in real time by the monitoring center, so as to obtain an environment scene identification result.
Wherein, intelligent environmental monitoring system based on deep learning still includes:
and the signal shielding module is used for shielding the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene when the environment scene identification result is the environment scene forbidden to use the networking communication equipment according to the obtained environment scene identification result.
Wherein the signal shielding module is further configured to:
and according to the obtained environment scene identification result, when the environment scene identification result is an environment scene which is not forbidden, namely the networking communication equipment is allowed to be used, closing and shielding the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene.
According to another aspect of the present invention, there is provided an intelligent environment monitoring device based on deep learning, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform any of the above intelligent environment monitoring methods based on deep learning.
According to still another aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, which when executed by a processor implements the intelligent environment monitoring method based on deep learning according to any one of the above.
It can be found that, in this embodiment, the network camera can shoot images of the environment space, and the monitoring center can collect and record images shot by the network camera where the remote environment space is located by the internet of things in real time, and the image recognition module can collect and record images shot by the network camera where the remote environment space is located by the internet of things in real time according to the monitoring center, recognize whether the current environment of the environment space is a scene where the use of the networked communication device is forbidden to obtain an environment scene recognition result, wherein the scene where the use of the networked communication device is forbidden includes scenes such as class or meeting, and effective monitoring of the environment can be realized.
Further, in this embodiment, the image recognition module may collect and record images shot by a network camera where the remote environment space is located in real time by the internet of things according to the monitoring center, and recognize whether the current environment of the environment space is a scene where the use of the networked communication device is prohibited to obtain an environment scene recognition result by using an algorithm analysis mode based on deep learning, so that it is possible to recognize whether the current environment of the environment space is a scene where the use of the networked communication device is prohibited to obtain an environment scene recognition result by using an algorithm analysis mode based on deep learning, and accuracy of scene recognition is improved.
Further, in this embodiment, the signal shielding module may shield the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene according to the obtained environment scene identification result, when the environment scene identification result is an environment scene in which the use of the networked communication device is prohibited, which is advantageous in that the learning or working efficiency of the personnel in the environment space can be effectively improved.
Further, in this embodiment, the signal shielding module may close the mobile phone signal and the wireless network signal shielding the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is an environment scene that is not prohibited, i.e., the networked communication device is allowed to be used, so that the advantages of humanization and labor and ease are combined, and the learning or working efficiency of the personnel in the environment space is further improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without any creative effort.
FIG. 1 is a schematic flow chart diagram of an embodiment of an intelligent environment monitoring method based on deep learning according to the present invention;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a deep learning-based intelligent environment monitoring method according to the present invention;
FIG. 3 is a schematic structural diagram of an embodiment of an intelligent environment monitoring system based on deep learning according to the present invention;
FIG. 4 is a schematic structural diagram of another embodiment of the intelligent environment monitoring system based on deep learning according to the present invention;
FIG. 5 is a schematic structural diagram of an embodiment of an intelligent environment monitoring device based on deep learning according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be noted that the following examples are only illustrative of the present invention, and do not limit the scope of the present invention. Similarly, the following examples are only some but not all examples of the present invention, and all other examples obtained by those skilled in the art without any inventive work are within the scope of the present invention.
The invention provides an intelligent environment monitoring method based on deep learning, which can realize effective environment monitoring.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating an embodiment of an intelligent environment monitoring method based on deep learning according to the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same. As shown in fig. 1, the method comprises the steps of:
s101: the network camera shoots images of the environment space.
S102: the monitoring center collects and records images shot by the network camera where the remote environment space is located in real time through the Internet of things.
S103: the image recognition module is used for recognizing whether the current environment of the environment space is a scene in which the use of the networking communication equipment is forbidden or not according to images shot by a network camera in which the remote environment space is located and collected and recorded by the Internet of things in real time by the monitoring center to obtain an environment scene recognition result; the scene for prohibiting the use of the networked communication device includes a class or a meeting.
Wherein, this image recognition module is according to the image that the network camera that this surveillance center is in real time gathered and record far-end environment space place by the thing networking, and whether discernment this environment space current environment is the scene of forbidding to use networking communications facilities obtains environment scene recognition result, can include:
the image recognition module is used for recognizing whether the current environment of the environment space is a scene forbidden to use the networking communication equipment or not by adopting an algorithm analysis mode based on deep learning according to the images shot by the network camera where the remote environment space is located, which are collected and recorded by the Internet of things in real time by the monitoring center, so as to obtain the environment scene recognition result.
In the embodiment, the deep learning is derived from artificial neural network research and is a deep learning structure containing multiple hidden layers and multiple layers of perceptrons. Deep learning may form a more abstract class or feature of high-level representation attributes by combining low-level features to discover a distributed feature representation of the data. Deep learning is a method based on characterization learning of data in machine learning. An observation, such as an image, may be represented in a number of ways, such as a vector of intensity values for each pixel, or more abstractly as a series of edges, a specially shaped region, and so forth. Tasks such as face recognition or facial expression recognition are more easily learned from the examples using some specific representation methods.
In this embodiment, deep learning is a new field in machine learning research, and its motivation is to establish and simulate a neural network for human brain to analyze and learn, which simulates the mechanism of human brain to interpret data such as images, sounds and texts. The deep learning is derived from artificial neural network research and is a deep learning structure containing multiple hidden layers and multiple layers of sensors. Deep learning may form a more abstract class or feature of high-level representation attributes by combining low-level features to discover a distributed feature representation of the data. Deep learning is a method based on characterization learning of data in machine learning. An observation, such as an image, may be represented in a number of ways, such as a vector of intensity values for each pixel, or more abstractly as a series of edges, a specially shaped region, and so forth. Tasks such as face recognition or facial expression recognition are more easily learned from the examples using some specific representation methods.
In this embodiment, the environment scene recognition result may be an environment scene in which the use of the networked communication device is prohibited in class or in a meeting, or may be an environment scene in which the use of the networked communication device is permitted in class or in a free discussion, which is not limited by the present invention.
Wherein, gather and record the image that the network camera that the far-end environmental space is located by the thing networking according to this surveillance center in real time at this image recognition module, after the scene of discerning whether this environmental space current environment is forbidden using networking communication equipment obtains environmental scene recognition result, can also include:
the signal shielding module shields the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is the environment scene forbidden to use the networking communication equipment, so that the advantage is that the learning or working efficiency of personnel in the environment space can be effectively improved.
It can be found that, in this embodiment, the network camera can shoot images of the environment space, and the monitoring center can collect and record images shot by the network camera where the remote environment space is located by the internet of things in real time, and the image recognition module can collect and record images shot by the network camera where the remote environment space is located by the internet of things in real time according to the monitoring center, recognize whether the current environment of the environment space is a scene where the use of the networked communication device is forbidden to obtain an environment scene recognition result, wherein the scene where the use of the networked communication device is forbidden includes scenes such as class or meeting, and effective monitoring of the environment can be realized.
Further, in this embodiment, the image recognition module may collect and record images shot by a network camera where the remote environment space is located in real time by the internet of things according to the monitoring center, and recognize whether the current environment of the environment space is a scene where the use of the networked communication device is prohibited to obtain an environment scene recognition result by using an algorithm analysis mode based on deep learning, so that it is possible to recognize whether the current environment of the environment space is a scene where the use of the networked communication device is prohibited to obtain an environment scene recognition result by using an algorithm analysis mode based on deep learning, and accuracy of scene recognition is improved.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating an intelligent environment monitoring method based on deep learning according to another embodiment of the present invention. In this embodiment, the method includes the steps of:
s201: the network camera shoots images of the environment space.
S202: the monitoring center collects and records images shot by the network camera where the remote environment space is located in real time through the Internet of things.
S203: the image recognition module is used for recognizing whether the current environment of the environment space is a scene in which the use of the networking communication equipment is forbidden or not according to images shot by a network camera in which the remote environment space is located and collected and recorded by the Internet of things in real time by the monitoring center to obtain an environment scene recognition result; the scene for prohibiting the use of the networked communication device includes a class or a meeting.
As described above in S103, which is not described herein.
S204: and the signal shielding module shields the mobile phone signals and the wireless network signals of the environmental space corresponding to the current environmental scene according to the obtained environmental scene identification result when the environmental scene identification result is the environmental scene forbidden to use the networking communication equipment.
Wherein, the signal shielding module shields the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is the environment scene forbidden to use the networking communication device, and may further include:
the signal shielding module closes and shields the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene when the environment scene identification result is the environment scene which is not forbidden, namely the networking communication equipment is allowed to be used according to the obtained environment scene identification result, so that the advantages of humanization and labor and ease combination can be achieved, and the learning or working efficiency of personnel in the environment space is further improved.
It can be found that, in this embodiment, the signal shielding module may shield the mobile phone signal and the wireless network signal of the environmental space corresponding to the current environmental scenario according to the obtained environmental scenario identification result, when the environmental scenario identification result is an environmental scenario in which the use of the networked communication device is prohibited, which is advantageous in that the learning or working efficiency of the personnel in the environmental space can be effectively improved.
Further, in this embodiment, the signal shielding module may close the mobile phone signal and the wireless network signal shielding the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is an environment scene that is not prohibited, i.e., the networked communication device is allowed to be used, so that the advantages of humanization and labor and ease are combined, and the learning or working efficiency of the personnel in the environment space is further improved.
The invention also provides an intelligent environment monitoring system based on deep learning, which can accurately prevent the robot from colliding with an operator.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an embodiment of an intelligent environment monitoring system based on deep learning according to the present invention. The intelligent environment monitoring system 30 based on deep learning comprises a network camera 31, a monitoring center 32 and an image recognition module 33.
The network camera 31 is used for capturing images of an environmental space.
The monitoring center 32 is configured to collect and record images shot by the network camera 31 in which the remote environment space is located in real time through the internet of things.
The image recognition module 33 is configured to recognize whether the current environment of the environment space is a scene where the use of the networked communication device is forbidden to obtain an environment scene recognition result according to an image captured by the network camera 31 where the remote environment space is located and recorded by the internet of things in real time by the monitoring center 32; the scene for prohibiting the use of the networked communication device includes a class or a meeting.
Optionally, the image recognition module 33 may be specifically configured to:
according to the images shot by the network camera 31 where the remote environment space is located, which are collected and recorded by the internet of things in real time by the monitoring center 32, whether the current environment of the environment space is a scene where the use of the networked communication equipment is forbidden is identified by adopting an algorithm analysis mode based on deep learning to obtain an environment scene identification result.
Referring to fig. 4, fig. 4 is a schematic structural diagram of another embodiment of the intelligent environment monitoring system based on deep learning according to the present invention. Different from the previous embodiment, the intelligent environment monitoring system 40 based on deep learning in this embodiment further includes: the signal shielding module 41.
The signal shielding module 41 is configured to shield the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene according to the obtained environment scene identification result, when the environment scene identification result is an environment scene where the use of the networked communication device is prohibited.
Optionally, the signal shielding module 41 is further configured to:
and according to the obtained environment scene identification result, when the environment scene identification result is an environment scene which is not forbidden, namely the networked communication equipment is allowed to be used, closing and shielding the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene.
Each unit module of the deep learning-based intelligent environment monitoring system 30/40 may respectively execute the corresponding steps in the above method embodiments, and therefore, the detailed description of each unit module is omitted here, and please refer to the description of the corresponding steps above.
The present invention further provides an intelligent environment monitoring device based on deep learning, as shown in fig. 5, including: at least one processor 51; and a memory 52 communicatively coupled to the at least one processor 51; the memory 52 stores instructions executable by the at least one processor 51, and the instructions are executed by the at least one processor 51 to enable the at least one processor 51 to execute the above-mentioned intelligent environment monitoring method based on deep learning.
Wherein the memory 52 and the processor 51 are coupled in a bus, which may comprise any number of interconnected buses and bridges, which couple one or more of the various circuits of the processor 51 and the memory 52 together. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other devices over a transmission medium. The data processed by the processor 51 is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor 51.
The processor 51 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And the memory 52 may be used to store data used by the processor 51 in performing operations.
The present invention further provides a computer-readable storage medium storing a computer program. The computer program realizes the above-described method embodiments when executed by a processor.
It can be found that, in this embodiment, the network camera can shoot images of the environment space, and the monitoring center can collect and record images shot by the network camera where the remote environment space is located by the internet of things in real time, and the image recognition module can collect and record images shot by the network camera where the remote environment space is located by the internet of things in real time according to the monitoring center, recognize whether the current environment of the environment space is a scene where the use of the networked communication device is forbidden to obtain an environment scene recognition result, wherein the scene where the use of the networked communication device is forbidden includes scenes such as class or meeting, and effective monitoring of the environment can be realized.
Further, in this embodiment, the image recognition module may collect and record images shot by a network camera where the remote environment space is located in real time by the internet of things according to the monitoring center, and recognize whether the current environment of the environment space is a scene where the use of the networked communication device is prohibited to obtain an environment scene recognition result by using an algorithm analysis mode based on deep learning, so that it is possible to recognize whether the current environment of the environment space is a scene where the use of the networked communication device is prohibited to obtain an environment scene recognition result by using an algorithm analysis mode based on deep learning, and accuracy of scene recognition is improved.
Further, in this embodiment, the signal shielding module may shield the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene according to the obtained environment scene identification result, when the environment scene identification result is an environment scene in which the use of the networked communication device is prohibited, which is advantageous in that the learning or working efficiency of the personnel in the environment space can be effectively improved.
Further, in this embodiment, the signal shielding module may close the mobile phone signal and the wireless network signal shielding the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is an environment scene that is not prohibited, i.e., the networked communication device is allowed to be used, so that the advantages of humanization and labor and ease are combined, and the learning or working efficiency of the personnel in the environment space is further improved.
In the several embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is merely one type of division of logical functions, and an actual implementation may have another division, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or may not be executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. With such an understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like.
The above description is only a partial embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent devices or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields using the contents of the present specification and the accompanying drawings are included in the scope of the present invention.
Claims (4)
1. An intelligent environment monitoring method based on deep learning is characterized by comprising the following steps:
the network camera shoots an image of an environment space;
the monitoring center collects and records images shot by the network camera in real time, wherein the remote environment space is located in the network camera;
the image identification module identifies whether the current environment of the environment space is a scene in which the use of the networking communication equipment is forbidden or not to obtain an environment scene identification result according to images shot by a network camera in which the remote environment space is located and collected and recorded by the Internet of things in real time by the monitoring center; wherein the scenario in which use of the networked communication device is prohibited includes a class or meeting scenario; the image recognition module is used for recognizing whether the current environment of the environment space is a scene in which the use of the networked communication equipment is forbidden or not by adopting an algorithm analysis mode based on deep learning according to images shot by a network camera in which the remote environment space is located and collected and recorded by the Internet of things in real time by the monitoring center to obtain an environment scene recognition result;
the signal shielding module shields mobile phone signals and wireless network signals of an environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is an environment scene forbidden to use the networking communication equipment;
the signal shielding module shields the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene according to the obtained environment scene identification result when the environment scene identification result is the environment scene forbidden to use the networking communication equipment, and the signal shielding module further comprises:
and the signal shielding module closes and shields the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene when the environment scene identification result is the environment scene which is not forbidden, namely the networking communication equipment is allowed to be used according to the obtained environment scene identification result.
2. An intelligent environmental monitoring system based on deep learning, comprising:
the system comprises a network camera, a monitoring center and an image identification module;
the network camera is used for shooting images of the environment space;
the monitoring center is used for acquiring and recording images shot by the network camera in which the remote environment space is located in real time through the Internet of things;
the image identification module is used for identifying whether the current environment of the environment space is a scene in which the use of the networked communication equipment is forbidden or not according to images shot by a network camera in which the remote environment space is located, which are collected and recorded by the Internet of things in real time by the monitoring center, so as to obtain an environment scene identification result; wherein the scenario in which use of the networked communication device is prohibited includes a class or meeting scenario;
wherein,
the image recognition module is specifically configured to:
according to images shot by a network camera where a remote environment space is located and collected and recorded by the monitoring center through the Internet of things in real time, whether the current environment of the environment space is a scene where the use of networking communication equipment is forbidden is identified by adopting an algorithm analysis mode based on deep learning to obtain an environment scene identification result;
the intelligent environment monitoring system based on deep learning further comprises:
the signal shielding module is used for shielding mobile phone signals and wireless network signals of an environment space corresponding to the current environment scene when the environment scene identification result is the environment scene forbidden to use the networking communication equipment according to the obtained environment scene identification result;
the signal shielding module is further configured to: and according to the obtained environment scene identification result, when the environment scene identification result is an environment scene which is not forbidden, namely the networking communication equipment is allowed to be used, closing and shielding the mobile phone signal and the wireless network signal of the environment space corresponding to the current environment scene.
3. An intelligent environmental monitoring device based on deep learning, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being
The at least one processor executes to enable the at least one processor to perform the intelligent environment monitoring method based on deep learning as recited in claim 1.
4. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the intelligent environment monitoring method based on deep learning of claim 1.
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